Abstract

We review a number of recently developed strategies for enhanced sampling of complex systems based on knowledge of the potential energy landscape. We describe four approaches, replica exchange, Kirkwood sampling, superposition-enhanced nested sampling, and basin sampling, and show how each of them can exploit information for low-lying potential energy minima obtained using basin-hopping global optimization. Characterizing these minima is generally much faster than equilibrium thermodynamic sampling, because large steps in configuration space between local minima can be used without concern for maintaining detailed balance.

Sponsorship

The authors gratefully acknowledge financial support from the EPSRC and the ERC. S.M acknowledges
financial support from the Gates Cambridge Scholarship.